Real-Time Detection of Diabetic Retinopathy Using Deep Learning Techniques

Irfan Ali Bhacho ()
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Irfan Ali Bhacho: Department of Computer Systems Engineering (Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan)

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 1848-1861

Abstract: Diabetic retinopathy is a prevalentdisease which is a medical condition frequently caused due to high sugar levels inthe blood. It deteriorates the optic nerve as it compresses and blurs the vision, which is used to detect white light and transmit signals to your cerebrum using a nerve. There has been a massive increase in the statistics having diabetic retinopathy which causes the loss of sight in any age group with no treatment Every diabetic patient is required to visit their ophthalmologist every two weeks or mandatorily in a month. Moreover, a bi-annual inspection is required to notice the amount of vision to see the objects. For this reason, Pakistan lacks ophthalmologists whoare expertsin their domain. Mostly, they are not available around the clock,especially in less privileged areas. Therefore, we have developed a smartphone-based handheld AI-integrated product thatis cost-effective and portable which detects visual Impairment and produces reports of the concernedpatient with a minor intervention on the same day by an eye specialist. This research project focuses on diabetic retinopathy detection by utilizing a 20D (20 Diopter) Lens and camera of any random smartphone thatcaptures fundus images which are further spitted and compared against various models of deep learning. In this research, VGG-15, ResNet50,and Custom CNN wereundertaken. As a result, VGG16 outperformed other models by obtaining the highest validation accuracy whichis 74.53% as well as the lowest validation loss of 55.94%. Moreover, ResNet50 yielded 74.08% validation accuracy and a computing validation loss of 58.72%. Consequently, the custom CNN Model achieves 57.26% validation accuracy and 57.26% validation loss. Thus, VGG16 performed best on the dataset provided and is deployed in the smartphone application which is a portable and cost-effective method for Diabetic Retinopathy screening in less privileged areas. This project aims to target three Sustainable Development Goals including Affordable and clean energy, good health and well-being, and Industry Innovation and Infrastructure respectively.

Keywords: Diabetic Retinopathy; Diabetes Mellitus VGG-16; ResNet50; Custom CNN Model (search for similar items in EconPapers)
Date: 2024
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